Despeckling Method for Single SAR Images Based on Zero-shot Learning

‍ ‍Speckle filtering is an important pre-processing step for synthetic aperture radar （SAR） image interpretation. In recent years， speckle-filtering methods based on convolutional neural networks （CNNs） have been rapidly developed. However， supervised learning based methods lack speckle-free reference SAR images as ground truth， and self-supervised learning based methods rely on multi-temporal SAR images from the same scene for speckle filtering. However， these additional auxiliary datasets are difficult to obtain in actual scenarios. In addition， self-supervised learning methods generally require large training datasets and deep networks for speckle filtering， resulting in high computational complexity. Therefore， a speckle filtering method for single SAR images based on zero-shot learning is proposed in this paper. The core idea of this method is to perform sublook decomposition on the test SAR image and select the paired sublook images closest to the test SAR image. It is theoretically proven that using paired sublook images for self-supervised training of the network achieves the same filtering effect as using speckle-free reference SAR images for supervised training of the network. Therefore， the self-supervised loss function is designed to quickly train the lightweight speckle-filtering network， and the trained network can be used for filtering the test SAR images. Compared with the speckle-filtering methods based on supervised learning and self-supervised learning， the proposed method does not require speckle-free reference or multi-temporal SAR images for model training nor additional training data. Speckle filtering can be implemented by using any lightweight CNN. Experimental results on the Radarsat-2 and ALOS-2 datasets show that the proposed method reduces the parameters by 22 times compared to the reference method； thus， it can better suppress speckles in homogeneous areas and preserve image details.